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Determining Pandemic Vulnerability Levels of Cities Using the Factor Analysis Method

Yıl 2022, Sayı: 44, 193 - 205, 08.07.2022
https://doi.org/10.26650/JGEOG2022-1057248

Öz

Pandemics have reentered our lives with the coronavirus disease (COVID-19), and the outbreak has affected all humanity on a global scale. Just as some countries in the world are more affected by this pandemic than others, although the number of cases and deaths is critically high in some cities in Turkey, others cities are less affected. This study aims to measure Turkish cities vulnerability levels to the pandemic based on variables that are likely to be influence a difference in the number of cases that emerge. A literature survey shows that similar studies in Turkey in particular are generally built on just one of the social, economic, and spatial vulnerability indices. No holistic approach has been found that combines all the relevant factors. This study uses 35 different variables gathered under the indicators of population, demography, urban life, economy, climate, environment and health, as identified at the end of the literature review. As a result, each city’s Pandemic Vulnerability Index score was calculated using factor analysis, and a hierarchical ranking was carried out among Turkish cities going from the most to the least vulnerable.

Kaynakça

  • Acharya, R., & Porwal, A. (2020). A vulnerability index for the management of and response to the COVID-19 epidemic in India: an ecological study. The Lancet Global Health, 8(9), 1142-1151. doi:10.1016/S2214-109X(20)30300-4 google scholar
  • Agrawal, N., Gupta, L., & Dixit, J. (2021). Assessment of the Socioeconomic Vulnerability to Seismic Hazards in the National Capital Region of India Using Factor Analysis. Sustainability, 13(17). doi:10.3390/su13179652 google scholar
  • Ahmadi, M., Sharifi, A., Dorosti, S., Ghoushchi, S. J., & Ghanbari, N. (2020). Investigation of effective climatology parameters on COVID-19 outbreak in Iran. Sci Total Environ, 729. doi:https://doi. org/10.1016/j.scitotenv.2020.138705 google scholar
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  • Almagro, M., & Orane-Hutchinson, A. (2020). JUE Insight: The determinants of the differential exposure to COVID-19 in New York city and their evolution over time. J Urban Econ. doi:https:// doi.org/10.1016/j.jue.2020.103293 google scholar
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  • Bashir, M. F., Ma, B., Bilal, Komal, B., Bashir, M. A., Tan, D., & Bashir, M. (2020). Correlation between climate indicators and COVID-19 pandemic in New York, USA,. Sci Total Environ, 728. doi:https:// doi.org/10.1016/j.scitotenv.2020.138835 google scholar
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Şehirlerin Pandemik Kırılganlık Seviyelerinin Faktör Analizi Yöntemiyle Belirlenmesi

Yıl 2022, Sayı: 44, 193 - 205, 08.07.2022
https://doi.org/10.26650/JGEOG2022-1057248

Öz

Yeni Koronavirüs Hastalığı (Covid-19) ile beraber pandemi kavramı yeniden hayatımıza girmiş, küresel ölçekteki salgın tüm insanlığı etkisi altına almıştır. Dünya’da bazı ülkelerin bu salgından daha fazla etkilenip diğerlerinin daha az zarar gördüğü gibi, Türkiye’de de bazı şehirlerde vaka ve vefat sayıları kritik derecede yüksek olmasına rağmen diğerleri daha az etkilenmiştir. Bu çalışmanın amacı, vaka sayılarındaki farklılıkların ortaya çıkmasında etkili olması muhtemel değişkenlerden yola çıkarak şehirlerimizin pandemiye karşı kırılganlık seviyelerini ölçmektir. Kırılganlık seviyesi yüksek olan illerimiz belirlenip bu bölgelere öncelik verildiğinde ve kırılganlığa yol açan sebepler tespit edilip gerekli çözümler üretilmeye başlandığında, şehirlerin salgına karşı direncinin artacağı ve vaka sayılarının azalmasına katkı sağlanacağı düşünülmektedir. Literatürde ve özellikle Türkiye’de gerçekleştirilen benzer çalışmaların genel olarak sosyal, ekonomik ve mekânsal kırılganlık indekslerinden biri üzerine kurgulandığı görülmüş, ilgili tüm faktörleri bir araya getiren bütüncül bir yaklaşıma rastlanmamıştır. Bu çalışmada literatür taraması neticesinde belirlenen ve nüfus, demografi, kentsel yaşam, ekonomi, iklim, çevre ve sağlık altyapısı göstergeleri altında toplanan 35 farklı değişken kullanılmış, faktör analizi yöntemiyle her şehrin Pandemik Kırılganlık İndeksi puanı hesaplanarak en kırılgan illerden en az kırılgan olanlara doğru indirgenen hiyerarşik bir sıralama gerçekleştirilmiştir. 

Kaynakça

  • Acharya, R., & Porwal, A. (2020). A vulnerability index for the management of and response to the COVID-19 epidemic in India: an ecological study. The Lancet Global Health, 8(9), 1142-1151. doi:10.1016/S2214-109X(20)30300-4 google scholar
  • Agrawal, N., Gupta, L., & Dixit, J. (2021). Assessment of the Socioeconomic Vulnerability to Seismic Hazards in the National Capital Region of India Using Factor Analysis. Sustainability, 13(17). doi:10.3390/su13179652 google scholar
  • Ahmadi, M., Sharifi, A., Dorosti, S., Ghoushchi, S. J., & Ghanbari, N. (2020). Investigation of effective climatology parameters on COVID-19 outbreak in Iran. Sci Total Environ, 729. doi:https://doi. org/10.1016/j.scitotenv.2020.138705 google scholar
  • Ali, I., & Alharbi, O. M. (2020). COVID-19: Disease, management, treatment, and social impact. Sci Total Environ, 728. doi:https://doi. org/10.1016/j.scitotenv.2020.138861 google scholar
  • Alirol, E., Getaz, L., Stoll, B., Chappuis, F., & Loutan, L. (2011). Urbanisation and Infectious Diseases in a Globalised World. Lancet Infect Dis, 131-141. google scholar
  • Almagro, M., & Orane-Hutchinson, A. (2020). JUE Insight: The determinants of the differential exposure to COVID-19 in New York city and their evolution over time. J Urban Econ. doi:https:// doi.org/10.1016/j.jue.2020.103293 google scholar
  • Andree, B. P. (2020). Incidence of Covid-19 and Connections with Air Pollution Exposure: Evidence from the Netherlands. SSRN. https:// ssrn.com/abstract=3584842 adresinden alındı google scholar
  • Arif, M., & Sengupta, S. (2021). Nexus between population density and novel coronavirus (COVID-19) pandemic in the south Indian states: A geo-statistical approach. Environ Dev Sustain, 23. doi:https://doi. org/10.1007/s10668-020-01055-8 google scholar
  • Atalay, A., Tortum, A., & Çodu, Y. M. (2014). Faktör Analizi Kullanılarak Trafik Kazalarının Modellenmesi. Uluslararası Trafik ve Ulaşım Güvenliği Dergisi, 1(1). google scholar
  • Auler, A., Cassaro, F., Silva, V d., & Pires, L. (2020). Evidence that high temperatures and intermediate relative humidity might favor the spread of COVID-19 in tropical climate: A case study for the most affected Brazilian cities. Sci Total Environ, 729. doi:https:// doi.org/10.1016/j.scitotenv.2020.139090 google scholar
  • Bashir, M. F., Ma, B., Bilal, Komal, B., Bashir, M. A., Tan, D., & Bashir, M. (2020). Correlation between climate indicators and COVID-19 pandemic in New York, USA,. Sci Total Environ, 728. doi:https:// doi.org/10.1016/j.scitotenv.2020.138835 google scholar
  • Bhadra, A., Mukherjee, A., & Sarkar, K. (2021). Impact of population density on Covid-19 infected and mortality rate in India. Model Earth Syst Environ, 7, 623-629. doi:https://doi.org/10.1007/s40808-020- 00984-7 google scholar
  • Bimtaş. (2020). Covid-19 salgını mücadele sürecinde İstanbul kırılganlık haritası proje raporu. İstanbul Kalkınma Ajansı. İstanbul: Kültür A.Ş. google scholar
  • Büyüköztürk, Ş. (2002). Faktör Analizi: Temel Kavramlar ve Ölçek Geliştirmede Kullanımı. Kuram ve Uygulamada Eğitim Yönetimi, (32), 470-483. google scholar
  • Chen, B., Liang, H., Yuan, X., Hu, Y., Xu, M., Zhao, Y., . . . Zhu, X. (2020). Roles of meteorological conditions in COVID-19 transmission on a worldwide scale. medRxiv. doi:https://doi. org/10.1101/2020.03.16.20037168 google scholar
  • Coşkun, H., Yıldırım, N., & Gündüz, S. (2021). The spread of COVID-19 virus through population density and wind in Turkey cities. Sci Total Environ. doi:https://doi.org/10.1016/j.scitotenv.2020.141663 google scholar
  • Doremalen, N. v., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., . . . Munster, V. J. (2020). Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1. N Engl J Med. doi:https://doi.org/10.1101/2020.03.09.20033217 google scholar
  • Eaton, P., Frank, B., Johnson, K., & Willoughby, S. (2019). Comparing exploratory factor models of the Brief Electricity and Magnetism Assessment and the Conceptual Survey of Electricity and Magnetism. Physical Review Physics Education Research, 15(2). doi:10.1103/PhysRevPhysEducRes.15.020133 google scholar
  • Fabrigar, L., & Wegener, D. (2012). Exploratory Factor Analysis. Oxford UniversityPress.doi:10.1093/acprof:osobl/9780199734177.001.0001 google scholar
  • Fattorini, D., & Regoli, F. (2020). Role of the chronic air pollution levels in the Covid-19 outbreak risk in Italy. Environ Pollut, 264. doi:https://doi.org/10.1016/j.envpol.2020.114732 google scholar
  • Flanagan, B., Gregory, E., Hallisey, E., Heitgerd, J., & Lewis, B. (2011). A Social Vulnerability Index for Disaster Management. Journal of Homeland Security and Emergency Management, 8(1). doi:10.2202/1547- 7355.1792 google scholar
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Toplam 71 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makalesi
Yazarlar

Cem Kırlangıçoğlu 0000-0002-5998-9496

Yayımlanma Tarihi 8 Temmuz 2022
Gönderilme Tarihi 13 Ocak 2022
Yayımlandığı Sayı Yıl 2022 Sayı: 44

Kaynak Göster

APA Kırlangıçoğlu, C. (2022). Şehirlerin Pandemik Kırılganlık Seviyelerinin Faktör Analizi Yöntemiyle Belirlenmesi. Journal of Geography(44), 193-205. https://doi.org/10.26650/JGEOG2022-1057248
AMA Kırlangıçoğlu C. Şehirlerin Pandemik Kırılganlık Seviyelerinin Faktör Analizi Yöntemiyle Belirlenmesi. Journal of Geography. Temmuz 2022;(44):193-205. doi:10.26650/JGEOG2022-1057248
Chicago Kırlangıçoğlu, Cem. “Şehirlerin Pandemik Kırılganlık Seviyelerinin Faktör Analizi Yöntemiyle Belirlenmesi”. Journal of Geography, sy. 44 (Temmuz 2022): 193-205. https://doi.org/10.26650/JGEOG2022-1057248.
EndNote Kırlangıçoğlu C (01 Temmuz 2022) Şehirlerin Pandemik Kırılganlık Seviyelerinin Faktör Analizi Yöntemiyle Belirlenmesi. Journal of Geography 44 193–205.
IEEE C. Kırlangıçoğlu, “Şehirlerin Pandemik Kırılganlık Seviyelerinin Faktör Analizi Yöntemiyle Belirlenmesi”, Journal of Geography, sy. 44, ss. 193–205, Temmuz 2022, doi: 10.26650/JGEOG2022-1057248.
ISNAD Kırlangıçoğlu, Cem. “Şehirlerin Pandemik Kırılganlık Seviyelerinin Faktör Analizi Yöntemiyle Belirlenmesi”. Journal of Geography 44 (Temmuz 2022), 193-205. https://doi.org/10.26650/JGEOG2022-1057248.
JAMA Kırlangıçoğlu C. Şehirlerin Pandemik Kırılganlık Seviyelerinin Faktör Analizi Yöntemiyle Belirlenmesi. Journal of Geography. 2022;:193–205.
MLA Kırlangıçoğlu, Cem. “Şehirlerin Pandemik Kırılganlık Seviyelerinin Faktör Analizi Yöntemiyle Belirlenmesi”. Journal of Geography, sy. 44, 2022, ss. 193-05, doi:10.26650/JGEOG2022-1057248.
Vancouver Kırlangıçoğlu C. Şehirlerin Pandemik Kırılganlık Seviyelerinin Faktör Analizi Yöntemiyle Belirlenmesi. Journal of Geography. 2022(44):193-205.